Deriving the Pricing Power of Product Features by Mining Consumer Reviews
AbstractThe increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumer-generated product reviews have become a valuable source of information for customers, who read the reviews and decide whether to buy the product based on the information provided. In this paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics that models consumer product reviews as elements in a tensor product of feature and evaluation spaces. We then impute the quantitative impact of consumer reviews on product demand as a linear functional from this tensor product space. We demonstrate how to use a low-dimension approximation of this functional to significantly reduce the number of model parameters, while still providing good experimental results. We evaluate our technique using a data set from Amazon.com consisting of sales data and the related consumer reviews posted over a 15-month period for 242 products. Our experimental evaluation shows that we can extract actionable business intelligence from the data and better understand the customer preferences and actions. We also show that the textual portion of the reviews can improve product sales prediction compared to a baseline technique that simply relies on numeric data.
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Bibliographic InfoPaper provided by NET Institute in its series Working Papers with number 07-36.
Length: 30 pages
Date of creation: Sep 2007
Date of revision:
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Web page: http://www.NETinst.org/
consumer reviews; e-commerce; econometrics; electronic commerce; electronic markets; hedonic analysis; Internet; opinion mining; product review; sentiment analysis; text mining; user-generated content.;
Find related papers by JEL classification:
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
- D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
- L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
- M31 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - Marketing
- M37 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - Advertising
- L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
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